import torch
import torch.nn as nn
import torch.nn.functional as F

# 通道注意力机制模块
class SEBlock(nn.Module):
    def __init__(self, channels, reduction=4):  # 减少reduction比例
        super(SEBlock, self).__init__()
        self.fc1 = nn.Linear(channels, channels // reduction, bias=False)
        self.fc2 = nn.Linear(channels // reduction, channels, bias=False)

    def forward(self, x):
        batch, channels, _, _ = x.size()
        y = F.adaptive_avg_pool2d(x, 1).view(batch, channels)
        y = F.relu(self.fc1(y))
        y = torch.sigmoid(self.fc2(y)).view(batch, channels, 1, 1)
        return x * y

# ResNet的基本块
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
        self.stride = stride
        self.se = SEBlock(out_channels)

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out = self.se(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

# ResNet模型
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=100):
        super(ResNet, self).__init__()
        self.in_channels = 16  # 进一步减少初始通道数
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 32, layers[0], stride=2)  # 第一层
        self.layer2 = self._make_layer(block, 64, layers[1], stride=2)  # 第二层
        self.layer3 = self._make_layer(block, 128, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 256, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * block.expansion),
            )

        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

def ResNet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])  # 每个layer只有1个block
